class_docstr: This class implements a model-specific automatic speech recognizer using the end-to-end speech recognizer
  DeepSpeech and PyTorch. It supports both version 2 and version 3 of DeepSpeech models as released at
  https://github.com/SeanNaren/deepspeech.pytorch.
  
  | Paper link: https://arxiv.org/abs/1512.02595
docstr: Fit the estimator on the training set `(x, y)`.
  
  :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
            could have different lengths. A possible example of `x` could be:
            `x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
  :param y: Target values of shape (nb_samples). Each sample in `y` is a string and it may possess different
            lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`.
  :param batch_size: Size of batches.
  :param nb_epochs: Number of epochs to use for training.
  :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
         and providing it takes no effect.
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code: def fit(
      self,
      x: np.ndarray,
      y: np.ndarray,
      batch_size: int = 128,
      nb_epochs: int = 10,
      **kwargs,
  ) -> None:
      """
      Fit the estimator on the training set `(x, y)`.
  
      :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
                could have different lengths. A possible example of `x` could be:
                `x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
      :param y: Target values of shape (nb_samples). Each sample in `y` is a string and it may possess different
                lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`.
      :param batch_size: Size of batches.
      :param nb_epochs: Number of epochs to use for training.
      :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
             and providing it takes no effect.
      """
      import random
  
      # Apply preprocessing
      x_preprocessed, _ = self._apply_preprocessing(x, None, fit=True)
      y_preprocessed = y
  
      x_in = np.empty(len(x_preprocessed), dtype=object)
      x_in[:] = list(x_preprocessed)
  
      # Put the model in the training mode
      self._model.train()
  
      if self.optimizer is None:  # pragma: no cover
          raise ValueError(
              "An optimizer is required to train the model, but none was provided."
          )
  
      # Train with batch processing
      num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size)))
      ind = np.arange(len(x_preprocessed)).tolist()
  
      # Start training
      for _ in range(nb_epochs):
          # Shuffle the examples
          random.shuffle(ind)
  
          # Train for one epoch
          for m in range(num_batch):
              # Batch indexes
              begin, end = (
                  m * batch_size,
                  min((m + 1) * batch_size, x_preprocessed.shape[0]),
              )
  
              # Extract random batch
              i_batch = np.empty(
                  len(x_preprocessed[ind[begin:end]]), dtype=object
              )
              i_batch[:] = list(x_preprocessed[ind[begin:end]])
              o_batch = y_preprocessed[ind[begin:end]]
  
              # Transform data into the model input space
              (
                  inputs,
                  targets,
                  input_rates,
                  target_sizes,
                  _,
              ) = self._transform_model_input(
                  x=i_batch, y=o_batch, compute_gradient=False
              )
  
              # Compute real input sizes
              input_sizes = input_rates.mul_(inputs.size(-1)).int()
  
              # Zero the parameter gradients
              self.optimizer.zero_grad()
  
              # Call to DeepSpeech model for prediction
              outputs, output_sizes, _ = self._model(
                  inputs.to(self._device), input_sizes.to(self._device)
              )
              outputs = outputs.transpose(0, 1)
  
              if self._version == 2:
                  outputs = outputs.float()
              else:
                  outputs = outputs.log_softmax(-1)
  
              # Compute the loss
              loss = self.criterion(
                  outputs, targets, output_sizes, target_sizes
              ).to(self._device)
  
              # Average the loss by mini batch if version 2 of DeepSpeech is used
              if self._version == 2:
                  loss = loss / inputs.size(0)
  
              # Actual training
              if self.use_amp:  # pragma: no cover
                  from apex import amp  # pylint: disable=E0611
  
                  with amp.scale_loss(
                      loss, self.optimizer
                  ) as scaled_loss:
                      scaled_loss.backward()
  
              else:
                  loss.backward()
  
              self.optimizer.step()
  
imports: import logging
  from typing import TYPE_CHECKING, List, Optional, Tuple, Union
  from pkg_resources import packaging
  import numpy
  from art import config
  from art.estimators.pytorch import PyTorchEstimator
  from art.estimators.speech_recognition.speech_recognizer import SpeechRecognizerMixin, PytorchSpeechRecognizerMixin
  from art.utils import get_file
label_desc: Use image node_id 4 for calling the PyTorchDeepSpeech obj's underlying member method code with example usage: obj.fit(x, y, batch_size, nb_epochs) without return types